Method and system for cardiac image segmentation

ABSTRACT

A system and method for cardiac image segmentation are provided. A plurality of slice images of a myocardium of a left ventricle at a plurality of time phases in a cardiac cycle may be obtained. An end-diastolic phase may be determined. A first slice image at the end-diastolic phase may be retrieved. A region of interest (ROI) in the first slice image may be obtained. A blood pool region in the ROI may be segmented. The ROI may be transformed into a polar coordinate image. A dual dynamic programming operation may be performed on the polar coordinate image to determine endocardial and epicardial boundaries of the myocardium in the polar coordinate image. The polar coordinate image may be transformed into a Cartesian coordinate image to obtain the endocardial and epicardial boundaries of the myocardium in the first slice image at the end-diastolic phase.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to Chinese Application No.201510974234.0, filed on Dec. 22, 2015, and Chinese Application No.201510974235.5, filed on Dec. 22, 2015, the entire contents of each ofwhich are hereby incorporated by reference.

TECHNICAL FIELD

The present disclosure generally relates to a method and system forimage processing, and more particularly, a method and system forsegmenting a left ventricle (LV) in medical images.

BACKGROUND

In recent years, cardiovascular diseases have become a leading cause ofdeath all over the world; millions of people get the cardiovasculardiseases and died. A cardiac function is an important indicator in thediagnosis of cardiovascular diseases. For example, quantitative analysisof global and regional measurements, such as ventricle volumes, ejectionfraction, and wall thickness may help a radiologist improve the accuracyand efficiency of the heart function evaluation. A CAD scheme may beused for locating endocardial and epicardial boundaries of the leftventricle in 4D cardiac cine MR images.

However, there are great variations in gray-scale and gradientdistributions in 4D cardiac magnetic resonance images of differentpatients, and a diversity of tissue adjacent to the epicardium of theleft ventricle. Such variations may bring about difficulty in accuratelydetecting the epicardium and endocardium of the left ventricle.

Heart perfusion magnetic resonance imaging techniques are widely used inheart disease diagnosis. However, MR images may include distortionand/or artifacts, which may cause misdiagnosis.

Great variations may exist in gray-scale and gradient distributions in4D cardiac magnetic resonance images, either obtained from a similarscanning region of different patients, or from different regions of asame patient. In addition, a simple threshold-based detection approachor gradient-based detection approach may be inadequate to accuratelyextract the left ventricle endocardium and epicardium, because of avariety of tissues adjacent to the epicardium of the left ventricle. Amodel-based detection approach may need a lot of training samples so asto obtain a satisfactory result suitable for automatic detection.

Thus, it is desirable to develop systems and methods for processing andcorrecting MR images, thereby improving the quality of cardiac MRimages.

SUMMARY

The present disclosure relates to MRI. One aspect of the presentdisclosure relates to a method for image processing. The method mayinclude one or more of the following operations.

One aspect of the present disclosure relates to a method for cardiacimage segmentation. The method may include one or more of the followingoperations. A plurality of slice images of a myocardium of a leftventricle at a plurality of time phases in a cardiac cycle may beobtained. Based on at least one of the plurality of slice images, anend-diastolic phase from the plurality of time phases may be determined.From the plurality of slice images, a first slice image at theend-diastolic phase may be retrieved. A region of interest in the firstslice image at the end-diastolic phase may be obtained. A blood poolregion in the region of interest in the first slice image at theend-diastolic phase may be segmented. The region of interest in thefirst slice image at the end-diastolic phase may be transformed into apolar coordinate image. A dual dynamic programming operation may beperformed on the polar coordinate image to determine endocardial andepicardial boundaries of the myocardium in the polar coordinate image.The polar coordinate image may be transformed into a Cartesiancoordinate image to obtain the endocardial and epicardial boundaries ofthe myocardium in the first slice image at the end-diastolic phase.

Another aspect of the present disclosure may relate to a method forcardiac image segmentation. The method may include one or more of thefollowing operations. A plurality of slice images of a myocardium of aleft ventricle at a plurality of time phases in a cardiac cycle may beobtained. The plurality of time phases may include a first time phase ofan end-diastolic phase and a second time phase different from the firsttime phase. Based on at least one of the plurality of slice images, theend-diastolic phase may be determined. From the plurality of sliceimages, a slice image corresponding to the first time phase and a sliceimage corresponding to the second time phase may be retrieved. A firstregion of interest in the slice image corresponding to the first timephase may be obtained. A first blood pool region in the first region ofinterest may be segmented. The first region of interest may betransformed into a first polar coordinate image. Based on a dual dynamicprogramming operation, endocardial and epicardial boundaries of themyocardium in the first polar coordinate image may be determined. Asecond region of interest in the slice image corresponding to the secondtime phase may be obtained. The second region of interest may betransformed into a second polar coordinate image. Endocardial andepicardial boundaries of the myocardium in the second polar coordinateimage may be determined based on the segmentation of the endocardial andepicardial boundaries of the myocardium in the first polar coordinateimage. The first polar coordinate image and the second polar coordinateimage may be transformed into Cartesian coordinate images to obtain theendocardial and epicardial boundaries of the myocardium in the sliceimage corresponding to the first time phase and the slice imagecorresponding to the second time phase.

A further aspect of the present disclosure relates to a non-transitorycomputer readable medium including executable instructions. Theinstructions, when executed by at least one processor, may cause the atleast one processor to effectuate a method for cardiac imagesegmentation.

A further aspect of the present disclosure relates to a system forcardiac image segmentation. The system may include at least oneprocessor and instructions. The instructions, when executed by the atleast one processor, may cause the at least one processor to effectuatea method for cardiac image segmentation. The system may further includea non-transitory computer readable medium including the instructions.

In some embodiments, the plurality of slice images may be acquired fromscanning of the left ventricle at the plurality of time phases.

In some embodiments, the determination of the end-diastolic phase fromthe plurality of time phases may include one or more of the followingoperations. Mid-ventricular slice images at the plurality of time phasesmay be selected. An initial region of interest in each of themid-ventricular slice images may be determined. A maximum intensityprojection image of the initial region of interest for themid-ventricular slice images may be obtained. A clustering operation maybe performed on the maximum intensity projection image to obtainprojection clusters. Based on the clustering operation, connectionregions may be identified from the projection clusters. Roundness ofeach of the connection regions may be determined. A connection regionwith the largest roundness may be identified from the connectionregions. A mean gray value of each of the mid-ventricular slice imagesin the connected region with the largest roundness may be determined.The end-diastolic phase may be determined based on a time phasecorresponding to one of the mid-ventricular slice images with a maximummean gray value.

In some embodiments, a center of the region of interest may coincidewith a centroid of the connection region with the largest roundness. Insome embodiments, a length of the region of interest may be longer thanthe length of a long axis of the connection region with the largestroundness.

In some embodiments, a region of interest in a mid-ventricular sliceimage at the end-diastolic phase may be obtained.

In some embodiments, Gamma correction may be performed on the region ofinterest in the mid-ventricular slice image at the end-diastolic phaseto obtain a corrected image.

In some embodiments, to determine candidate areas may include one ormore of the following operations. A Fuzzy c-means clustering operationmay be performed on the corrected image to obtain clusters of areas.Brightness of each of the clusters may be determined. A cluster havingthe highest brightness may be identified among the clusters of areas.Areas belonging the cluster having the highest brightness may bedesignated as candidate areas.

In some embodiments, to segment a blood pool region may include one ormore of the following operations. An overlapping area where thecandidate area overlaps the connection region with the largest roundnessfor each of the candidate areas may be determined. The candidate areawith the largest overlapping area among the determined overlapping areasas the blood pool region in the mid-ventricular slice image at theend-diastolic phase may be designated.

In some embodiments, to segment a blood pool region may include one ormore of the following operations. A region of interest in a second sliceimage at the end-diastolic phase other than the mid-ventricular sliceimage at the end-diastolic phase may be obtained. A blood pool region inthe region of interest in the second slice image at the end-diastolicphase may be segmented.

In some embodiments, the segmentation of the blood pool region in theregion of interest in the second slice image at the end-diastolic phaseother than the mid-ventricular slice image may include using thesegmentation of the blood pool region in the mid-ventricular slice imageat the end-diastolic phase as guidance.

In some embodiments, the segmentation of the blood pool region in theregion of interest in the mid-ventricular slice image at theend-diastolic phase and the segmentation of the blood pool region in theregion of interest of the second slice image at the end-diastolic phasemay be performed in an order from the mid-ventricular slice image at theend-diastolic phase to an apical slice image at the end-diastolic phase.

In some embodiments, the segmentation of the blood pool region in theregion of interest of the mid-ventricular slice image at theend-diastolic phase and the segmentation of the blood pool region in theregion of interest in the second slice image at the end-diastolic phasemay be performed in an order from the mid-ventricular slice image at theend-diastolic phase to a basal slice image at the end-diastolic phase.

In some embodiments, the endocardial and epicardial boundaries of themyocardium in the slice image corresponding to the first time phase orthe slice image corresponding to the second time phase may beconvex-hulled and smoothed.

In some embodiments, mid-ventricular slice images of the myocardium ofthe left ventricle at the plurality of time phases may be obtained.

In some embodiments, to obtain a plurality of slice images of themyocardium of the left ventricle may include one or more of thefollowing operations. A first mid-ventricular slice image at the firsttime phase may be obtained. A second mid-ventricular slice image at thesecond time phase may be obtained. A third mid-ventricular slice imageat a third time phase may be obtained. The first time phase, the secondtime phase, and the third time phase may be at an anti-chronologicalorder in the plurality of time phases.

In some embodiments, the determination of the endocardial and epicardialboundaries of the myocardium may include one or more of the followingoperations. A third region of interest in the third mid-ventricularslice image may be obtained. A fourth region of interest in the firstmid-ventricular slice image may be obtained. A fifth region of interestin the second mid-ventricular slice image may be obtained. The thirdregion of interest may be transformed into a third polar coordinateimage. The fourth region of interest may be transformed into a fourthpolar coordinate image. The fifth region of interest may be transformedinto a fifth polar coordinate image. Endocardial and epicardialboundaries of the myocardium in the fourth polar coordinate image may bedetermined. Endocardial and epicardial boundaries of the myocardium inthe fifth polar coordinate image may be determined. Endocardial andepicardial boundaries of the myocardium in the third polar coordinateimage may be determined based on the determination of the endocardialand epicardial boundaries of the myocardium in the fourth polarcoordinate image and the fifth polar coordinate image.

Additional features will be set forth in part in the description whichfollows, and in part will become apparent to those skilled in the artupon examination of the following and the accompanying drawings or maybe learned by production or operation of the examples. The features ofthe present disclosure may be realized and attained by practice or useof various aspects of the methodologies, instrumentalities andcombinations set forth in the detailed examples discussed below.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further described in terms of exemplaryembodiments. These exemplary embodiments are described in detail withreference to the drawings. The drawings are not to scale. Theseembodiments are non-limiting exemplary embodiments, in which likereference numerals represent similar structures throughout the severalviews of the drawings, and wherein:

FIG. 1 illustrates a block diagram of an imaging system 100 according tosome embodiments of the present disclosure;

FIG. 2 illustrates an architecture of an exemplary computing device 200according to some embodiments of the present disclosure;

FIG. 3 illustrates a block diagram of an exemplary processor 300according to some embodiments of the present disclosure;

FIG. 4 illustrates a flowchart of an exemplary process 400 forsegmenting the epicardium and the endocardium of a left ventricle in aslice image at an end-diastolic phase according to some embodiments ofthe present disclosure;

FIG. 5 illustrates a flowchart of an exemplary process 500 fordetermining the end-diastolic phase according to some embodiments of thepresent disclosure;

FIG. 6 illustrates a flowchart of an exemplary process 600 forsegmenting a blood pool region according to some embodiments of thepresent disclosure;

FIG. 7 illustrates a flowchart of an exemplary process 700 forsegmenting a blood pool region according to some embodiments of thepresent disclosure;

FIG. 8 illustrates a flowchart of an exemplary process 800 forsegmenting the epicardium and the endocardium of a left ventricleaccording to some embodiments of the present disclosure;

FIG. 9 illustrates a flowchart of an exemplary process 900 forsegmenting the endocardium and the epicardium of a left ventricle in theslice images at the time phases other than the end-diastolic phaseaccording to some embodiments of the present disclosure;

FIG. 10A illustrates an exemplary initial region of interest accordingto some embodiments of the present disclosure;

FIG. 10B illustrates an exemplary maximum intensity projection image inthe initial region of interest according to some embodiments of thepresent disclosure;

FIG. 10C illustrates an exemplary binary image of the maximum intensityprojection image obtained according to the Fuzzy c-means clusteringalgorithm according to some embodiments of the present disclosure;

FIG. 10D illustrates an exemplary blood pool region segmentation resultshown in the region of interest according to some embodiments of thepresent disclosure;

FIG. 11A through FIG. 11C illustrate exemplary diagrams of blood poolregion segmentation according to some embodiments of the presentdisclosure; and

FIG. 12A through FIG. 12O illustrate exemplary segmentation results ofan epicardium and an endocardium in three representative slice images atfive time phases.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth by way of examples in order to provide a thorough understanding ofthe relevant disclosure. However, it should be apparent to those skilledin the art that the present disclosure may be practiced without suchdetails. In other instances, well known methods, procedures, systems,components, and/or circuitry have been described at a relativelyhigh-level, without detail, in order to avoid unnecessarily obscuringaspects of the present disclosure. Various modifications to thedisclosed embodiments will be readily apparent to those skilled in theart, and the general principles defined herein may be applied to otherembodiments and applications without departing from the spirit and scopeof the present disclosure. Thus, the present disclosure is not limitedto the embodiments shown, but to be accorded the widest scope consistentwith the claims.

It will be understood that the term “system,” “engine,” “unit,”“module,” and/or “block” used herein are one method to distinguishdifferent components, elements, parts, section or assembly of differentlevel in ascending order. However, the terms may be displaced by otherexpression if they may achieve the same purpose.

It will be understood that when a unit, engine, module or block isreferred to as being “on,” “connected to,” or “coupled to” another unit,engine, module, or block, it may be directly on, connected or coupledto, or communicate with the other unit, engine, module, or block, or anintervening unit, engine, module, or block may be present, unless thecontext clearly indicates otherwise. As used herein, the term “and/or”includes any and all combinations of one or more of the associatedlisted items.

The terminology used herein is for the purposes of describing particularexamples and embodiments only, and is not intended to be limiting. Asused herein, the singular forms “a,” “an,” and “the” may be intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “include” and/or“comprise,” when used in this disclosure, specify the presence ofintegers, devices, behaviors, stated features, steps, elements,operations, and/or components, but do not exclude the presence oraddition of one or more other integers, devices, behaviors, features,steps, elements, operations, components, and/or groups thereof.

The present disclosure relates to systems and methods for cardiac imagesegmentation. The cardiac image may be a MR image, a CT image, etc.According to some embodiments of the present disclosure, the method mayinclude one or more of the following operations. A plurality of sliceimages of a myocardium of a left ventricle at a plurality of time phasesin a cardiac cycle may be obtained. Based on at least one of theplurality of slice images, an end-diastolic phase from the plurality oftime phases may be determined. From the plurality of slice images, afirst slice image at the end-diastolic phase may be retrieved. A regionof interest in the first slice image at the end-diastolic phase may beobtained. A blood pool region in the region of interest in the firstslice image at the end-diastolic phase may be segmented. The region ofinterest in the first slice image at the end-diastolic phase may betransformed into a polar coordinate image. A dual dynamic programmingoperation may be performed on the polar coordinate image to determineendocardial and epicardial boundaries of the myocardium in the polarcoordinate image. The polar coordinate image may be transformed into aCartesian coordinate image to obtain the endocardial and epicardialboundaries of the myocardium in the first slice image at theend-diastolic phase.

Based on the method described herein, the cardiac image (e.g., a MRimage) may be processed automatically without user intervention so thatthe blood pool region, the epicardium and/or the endocardium of amyocardium may be segmented automatically.

FIG. 1 illustrates an exemplary imaging system according to someembodiments of the present disclosure. An imaging system may produce animage of an object (e.g., a heart). As illustrated, the imaging systemmay include an imaging device 110, a controller 120, a data processingsystem 130, and an input/output device 140.

The imaging device 110 may scan an object and generate a plurality ofdata relating to the object. The imaging device 110 may furtherreconstruct an image from the plurality of data. In some embodiments,the imaging device 110 may be a medical imaging device, for example, aPET device, a SPECT device, a CT device, an MRI device, or the like, orany combination thereof (e.g., a PET-CT device, a PET-MRI device, or aSPECT-MRI device). In some embodiments, the imaging device 110 mayinclude a scanner to scan an object and obtain information related withthe object. In some embodiments, the imaging device 110 may be aradioactive scanning device. The radioactive scanning device may includea radioactive scanning source to emit radioactive rays to the objectbeing scanned. The radioactive rays may include, for example, particlerays, photon rays, or the like, or any combination thereof. The particlerays may include neutron, proton, α-ray, electron, μ-meson, heavy ion,or the like, or any combination thereof. The photon rays may includeX-ray, γ-ray, ultraviolet, laser, or the like, or any combinationthereof. In some embodiments, the photon ray may be X-ray, and theimaging device 110 may be a CT system, a digital radiography (DR)system, a multi-modality system, or the like, or any combinationthereof. Exemplary multi-modality system may include a computedtomography-positron emission tomography (CT-PET) system, a computedtomography-magnetic resonance imaging (SPECT-MRI) system, or the like.

The controller 120 may control the imaging device 110, the input/outputdevice 140, and/or the data processing system 130. The controller 120may receive information from or send information to the imaging device110, the input/output device 140, and/or the data processing system 130.For example, the controller 120 may receive commands from theinput/output device 140 provided by a user. As another example, thecontroller 130 may process data input by a user via the input/outputunit 140 and transform the data into one or more commands. As stillanother example, the controller 120 may control the imaging device 110,the input/output device 140, and/or the data processing system 130according to the received commands or transformed commands. As stillanother example, the controller 120 may receive image signals or datarelated to an object from the imaging device 110. As still anotherexample, the controller 120 may send image signals or data to the dataprocessing system 130. As still another example, the controller 120 mayreceive processed data or constructed image from the data processingsystem 130. As still another example, the controller 120 may sendprocessed data or constructed image to the input/output device 140 fordisplaying. In some embodiments, the controller 120 may include acomputer, a program, an algorithm, a software, a storage device, one ormore interfaces, etc. Exemplary interfaces may include the interfaces ofthe imaging device 110, the input/output device 140, the data processingsystem 150, and/or other modules or units in the imaging system.

In some embodiments, the controller 120 may receive a command providedby a user including, for example, an imaging technician, a doctor, etc.Exemplary commands may relate to a scan time, a location of the object,the location of a couch on which the object lies, objection or arotating speed of the gantry, a specific parameter relating to athreshold that may be used in the image reconstruction process, or thelike, or any combination thereof. In some embodiments, the controller120 may control the data processing system 130 to select differentalgorithms to process the raw data of an image.

The data processing system 130 may process information received from theimaging device 110, the controller 120, the network 170 and/or theinput/output device 140. The data processing system 130 may deliver theimages to the input/output device 140 for display. In some embodiments,the data processing system 130 may perform operations including, forexample, data preprocessing, image reconstruction, image correction,image composition, image enhancement, image transformation, imagesegmentation, or the like, or any combination thereof. In someembodiments, the data processing system 130 may process data based on atechnique including, for example, a clustering technique, a correctionalgorithm, a reconstruction technique, or the like, or any combinationthereof. In some embodiments, image data regarding a myocardium may beprocessed in the data processing system 130.

In some embodiments, the data processing system 130 may generate acontrol signal relating to the configuration of the imaging device 110.In some embodiments, the result generated by the data processing system130 may be provided to other modules or units in the system including,e.g., a database (not shown), a terminal (not shown) via the network170. In some embodiments, the data from the data processing system 130may be transmitted to a storage (not shown) for storing.

The input/output device 140 may receive or output information. In someembodiments, the input/output device 140 may include a keyboard, a touchscreen, a mouse, a remote controller, or the like, or any combinationthereof. The input and/or output information may include programs,software, algorithms, data, text, number, images, voices, or the like,or any combination thereof. For example, a user may input some initialparameters or conditions to initiate an imaging process. As anotherexample, some information may be imported from an external resourceincluding, for example, a floppy disk, a hard disk, a wired terminal, awireless terminal, or the like, or any combination thereof. The outputinformation may be transmitted to a display, a printer, a storagedevice, a computing device, or the like, or a combination thereof. Insome embodiments, the input/output device 140 may include a graphicaluser interface. The graphical user interface may facilitate a user toinput parameters, and intervene in the data processing procedure. Insome embodiments, a slice image or a segmented slice image of amyocardium of a left ventricle may be displayed on the graphical userinterface. For example, an image of a blood pool region 150 or asegmented endocardium and epicardium of the myocardium 160 may bedisplayed on the graphical user interface.

In some embodiments, the imaging device 110, the controller 120, thedata processing system 130, the input/output device 140 may be connectedto or communicate with each other directly. In some embodiments, theimaging device 110, the controller 120, the data processing system 130,the input/output device 140 may be connected to or communicate with eachother via a network 170. In some embodiments, the imaging device 110,the controller 120, the data processing system 130, the input/outputdevice 140 may be connected to or communicate with each other via anintermediate unit (not shown in FIG. 1). The intermediate unit may be avisible component or an invisible field (radio, optical, sonic,electromagnetic induction, etc.). The connection between different unitsmay be wired or wireless. The wired connection may include using a metalcable, an optical cable, a hybrid cable, an interface, or the like, orany combination thereof. The wireless connection may include using aLocal Area Network (LAN), a Wide Area Network (WAN), a Bluetooth, aZigBee, a Near Field Communication (NFC), or the like, or anycombination thereof. The network 170 that may be used in connection withthe present system described herein are not exhaustive and are notlimiting.

It should be noted that the above description about the imaging systemis merely an example, and should not be understood as the onlyembodiment. To those skilled in the art, after understanding the basicprinciples of the connection between different units, the units andconnection between the units may be modified or varied without departingfrom the principles. The modifications and variations are still withinthe scope of the current application described above. In someembodiments, these units may be independent, and in some embodiments,part of the units may be integrated into one unit to work together.

FIG. 2 illustrates an exemplary architecture of a computing device 200for image processing. Computing device 200 may be a general purposecomputer or a special purpose computer, configured to perform thefunctions disclosed in this application. For example, image segmentationmay be implemented on computing device 200, via its hardware, softwareprogram, firmware, or a combination thereof. Although only one suchcomputer is shown, for convenience, the computer functions relating toimage processing as described herein may be implemented in a distributedfashion on a number of similar platforms, to distribute the processingload.

Computing device 200, for example, may include a communication port 130configured to facilitate communications between computing device 200 andother devices via, for example, a network (wired or wireless). Computingdevice 200 may also include a processor 240 configured to executeprogram instructions stored in a storage device (e.g., disk 210, ROM250, and RAM 260) or a non-transitory computer-readable medium. Whenprocessor 240 executes the program instructions, computing device may becaused to perform one or more functions disclosed in this application.For example, processor 240 may performing one or more operations on MRimages. The operations may include image manipulation (e.g., rotating,flipping, resizing, or cropping), image segmentation, image correction,image registration, image matching, image partition, image smoothing, orthe like, or a combination thereof.

Processor 240 may include or is part of one or more known processingdevices such as a microprocessor. In some embodiments, processor 240 mayinclude any type of single or multi-core processor, mobile devicemicrocontroller, central processing unit, etc.

Computing device 200 may further include an internal communication bus270, program storage, and data storage of different forms, such as, disk210, read only memory (ROM) 250, or random access memory (RAM) 260, forvarious data files to be processed and/or communicated by the computer,as well as possibly program instructions to be executed by processor240. Computing device 200 may also include an I/O component 220,supporting input/output flows between the computing device 200 and othercomponents therein such as user interface elements (not shown infigures). Computing device 200 may also receive programming and data vianetwork communications.

Hence, aspects of the methods of the image processing and/or otherprocesses, as described herein, may be embodied in programming. Programaspects of the technology may be thought of as “products” or “articlesof manufacture” typically in the form of executable code and/orassociated data that is carried on or embodied in a type of machinereadable medium. Tangible non-transitory “storage” type media includeany or all of the memory or other storage for the computers, processors,or the like, or associated modules thereof, such as varioussemiconductor memories, tape drives, disk drives and the like, which mayprovide storage at any time for the software programming.

All or portions of the software may at times be communicated through anetwork such as the Internet or various other telecommunicationnetworks. Such communications, for example, may enable loading of thesoftware from one computer or processor into another, for example, froma management server or host computer of a scheduling system into thehardware platform(s) of a computing environment or other systemimplementing a computing environment or similar functionalities inconnection with image processing. Thus, another type of media that maybear the software elements includes optical, electrical andelectromagnetic waves, such as used across physical interfaces betweenlocal devices, through wired and optical landline networks and overvarious air-links. The physical elements that carry such waves, such aswired or wireless links, optical links or the like, also may beconsidered as media bearing the software. As used herein, unlessrestricted to tangible “storage” media, terms such as computer ormachine “readable medium” refer to any medium that participates inproviding instructions to a processor for execution.

A machine-readable medium may take many forms, including but not limitedto, a tangible storage medium, a carrier wave medium or physicaltransmission medium. Non-volatile storage media include, for example,optical or magnetic disks, such as any of the storage devices in anycomputer(s), or the like, which may be used to implement the system orany of its components shown in the drawings. Volatile storage media mayinclude dynamic memory, such as a main memory of such a computerplatform. Tangible transmission media may include coaxial cables; copperwire and fiber optics, including the wires that form a bus within acomputer system. Carrier-wave transmission media may take the form ofelectric or electromagnetic signals, or acoustic or light waves such asthose generated during radio frequency (RF) and infrared (IR) datacommunications. Common forms of computer-readable media may include, forexample: a floppy disk, a flexible disk, hard disk, magnetic tape, anyother magnetic medium, a CD-ROM, DVD or DVD-ROM, any other opticalmedium, punch cards paper tape, any other physical storage medium withpatterns of holes, a RAM, a PROM and EPROM, a FLASH-EPROM, any othermemory chip or cartridge, a carrier wave transporting data orinstructions, cables or links transporting such a carrier wave, or anyother medium from which a computer may read programming code and/ordata. Many of these forms of computer readable media may be involved incarrying one or more sequences of one or more instructions to a physicalprocessor for execution.

Those skilled in the art will recognize that the present teachings areamenable to a variety of modifications and/or enhancements. For example,although the implementation of various components described herein maybe embodied in a hardware device, it may also be implemented as asoftware only solution—e.g., an installation on an existing server. Inaddition, image processing and segmentation as disclosed herein may beimplemented as a firmware, firmware/software combination,firmware/hardware combination, or a hardware/firmware/softwarecombination.

FIG. 3 illustrates a block diagram of an exemplary processor 300according to some embodiments of the present disclosure. The processor300 may include an image acquisition module 302, a time phasedetermination module 304, a gray value determination module 306, acluster module 308, an image correction module 310, a point processingmodule 312, a boundary determination module 314, an image enhancementmodule 316, an image transformation module 318, an image segmentationmodule 320, or other modules. In some embodiments, the modules may beindependent. In some embodiments, part of the modules may be integratedinto one module to work together.

The image acquisition module 302 may obtain an image of a myocardium ofa (left) ventricle. In some embodiments, an image of the myocardium maybe divided into a plurality of slices, and the image acquisition module302 may obtain slice images of the myocardium. The plurality of slicesmay include a mid-ventricular slice of the myocardium. Themid-ventricular slice may refer to a slice in the middle of the (left)ventricular. The slice images may include one or more images of themid-ventricular slice (also referred to herein as mid-ventricular sliceimages) at different time phases. In some embodiments, the imageacquisition module 302 may obtain the image of the myocardium atdifferent cardiac cycle. The cardiac cycle may refer to a duration from(left) ventricular systole to (left) ventricular diastole. A cardiaccycle may be divided into a number of time segments. The time segmentsmay be discrete and/or consecutive. A time segment of the cardiac cyclemay be referred to herein as a time phase. In some embodiments, the timephases may have a same length of time or different lengths of time. Thenumber of time phases in a cardiac cycle may be a predetermined valuebetween one and thirty. For instance, the number of time phases in thecardiac cycle may be five, ten, twenty, etc. In some embodiments, theimage acquisition module 302 may obtain the image of the myocardium ineach of the plurality of slices at each of the time phases. Theplurality of time phases may include an end-diastolic phase. Theend-diastolic phase may refer to the last time phase of the leftventricular diastole in a cardiac cycle. The image acquisition module302 may obtain one or more slice images at the end-diastolic phase.

The time phase determination module 304 may determine the end-diastolicphase based on the slice images obtained by the image acquisition module302. In some embodiments, the time phase determination module 304 maydetermine an initial region of interest in each of the mid-ventricularslice images from a plurality of slice images. The time phasedetermination module 304 may also determine a maximum intensityprojection (MIP) image of the initial region of interest for themid-ventricular slice images. The time phase determination module 304may also obtain information from the gray value determination module 306and the cluster module 308. Based on the information, the time phasedetermination module 304 may determine the end-diastolic phase.

The gray value determination module 306 may determine gray values ofdifferent regions in the slice images. In some embodiments, the grayvalue determination module 306 may also determine a gray valuedistribution of the slice images at some time phases. The gray value orgray value distribution may be transferred to other modules. Forexample, the gray values of different regions may be transferred to thetime phase determination module 304 to determine the end-diastolicphase. As another example, the gray value distribution of the sliceimages may be transferred to image enhancement module 316 to enhance animage.

The cluster module 308 may perform a clustering operation on an image.The image may include a slice image, a region of interest in a sliceimage, a maximum intensity projection image, a corrected image obtainedby correcting a slice image. The clustering operation may be based on ak-means clustering algorithm, a Fuzzy-c mean clustering algorithm, ahierarchical clustering algorithm, a Gaussian clustering algorithm, aminimal spamming tree (MST) based clustering algorithm, a kernel k-meansclustering algorithm, a density based clustering algorithm, or the like.In some embodiments, after the clustering operation, the image may bedivided into a predetermined number of clusters. The number of theclusters may be between two and ten. As one example, a corrected sliceimage may be divided into two clusters with different values ofbrightness. As another example, a maximum intensity projection image maybe divided into more than one cluster with different values ofbrightness, e.g., two clusters, three clusters, four clusters, etc.

The image correction module 310 may correct a slice image to generate acorrected image based on a correction algorithm, e.g., Gamma correction.The corrected image may be transferred to the cluster module 308.

The point processing module 312 may process points (e.g., edgepoints/pixels of a blood pool region, points at radial lines). A regionof interest may be scanned using a plurality of radial lines to segmenta blood pool region. The radial lines may intersect with the edge of theblood pool region. In some embodiments, the point processing module 312may extract an edge point from a radial line. An edge point may be awayfrom the centroid of the blood pool region by a radial distance. In someembodiments, the point processing module 312 may further extract someedge points from all of the edge points. For example, the pointprocessing module 312 may extract some edge points with the nearestradial distance. The number of the extracted edge points may be betweenone and one hundred. In some embodiments, the point processing module312 may also remove one or more edge points. For instance, an edge pointwhose radial distance is larger than a threshold (e.g., a sum of themean value and the standard deviation of the extracted edge points) maybe removed. The extracted edge points or the remaining (or referred toas filtered) edge points may be transferred to the boundarydetermination module 314 to determine the boundary (edge) of the bloodpool region.

The boundary determination module 314 may determine a region boundarybased on, e.g., edge points. For instance, the boundary determinationmodule 314 may determine the boundary of a blood pool region, theboundary of the endocardium of a myocardium, a boundary of theepicardium of a myocardium, etc. In some embodiments, the boundarydetermination module 314 may interpolate the extracted or filtered edgepoints determined by the point processing module 312. In someembodiments, the boundary determination module 314 may generate, by wayof, e.g., fitting, a closed curve based on the extracted or filterededge points and/or the points determined by way of the interpolation.

The image enhancement module 316 may enhance an image. The enhancementmay be based on a gray value of the image and/or a gray valuedistribution associated with a plurality of related images. Merely byway of example, to obtain an enhanced image of a slice image of themyocardium of the left ventricle at a specific time phase, the imageenhancement module 316 may retrieve a gray value distribution of sliceimages of the myocardium at several time phases, and then multiply thegray value of a pixel in the slice image at the specific time phase bythe value of the gray value distribution corresponding to the gray valueof the pixel to obtain the enhanced image. The enhanced image may betransferred to the image transformation module 318 or the imagesegmentation module 320.

The image transformation module 318 may transform a Cartesian coordinateimage into a polar coordinate image. In some embodiments, the Cartesiancoordinate image may include a slice image, an enhanced image of a sliceimage, or a region of interest in a slice image, or a region of interestof an enhanced image of a slice image, or the like. In some embodiments,the image transformation module 318 may transform a polar coordinateimage into a Cartesian coordinate image.

The image segmentation module 320 may segment a region from an image.For instance, the image segmentation module 320 may segment a blood poolregion from a slice image. As another example, the image segmentationmodule 320 may segment the endocardium and/or an epicardium of themyocardium. In some embodiments, the image segmentation module 320 maysegment a blood pool region in one slice image based on a segmentationof a blood pool region in another slice image. In some embodiments, theimage segmentation module 320 may segment the endocardium and/or theepicardium of a myocardium in the slice images at the time phases otherthan the end-diastolic phase based on the segmentation of theendocardium and/or the epicardium of the myocardium in the slice imagesat the end-diastolic phase.

For brevity, an image or a portion thereof corresponding to an object(e.g., an organ, a tissue, etc.) may be referred to as the object. Forinstance, a portion of a slice image of an endocardium may be referredto as an endocardium. As another example, the segmentation of a portionof the slice image of the endocardium may be referred to as thesegmentation of the endocardium.

It should be noted that the above description about the processor 300 ismerely an example, and should not be understood as the only embodiment.To those skilled in the art, after understanding the basic principles ofthe connection between different modules, the modules and connectionbetween the modules may be modified or varied without departing from theprinciples. In some embodiments, these modules may be independent, andin some embodiments, part of the modules may be integrated into onemodule to work together. For example, the point processing module 312and the boundary determination module 314 may be included in the imagesegmentation module 320. The modifications and variations are stillwithin the scope of the current application described above.

FIG. 4 illustrates a flowchart of an exemplary process 400 forsegmenting the epicardium and the endocardium of a myocardium of a leftventricle in a slice image at an end-diastolic phase in accordance withsome embodiments of the present disclosure. In some embodiments, atleast part of process 400 may be performed by processor 240.

In 410, a plurality of slice images (MR images or CT images) of a leftventricle at a plurality of time phases in a cardiac cycle may beobtained. In some embodiments, the operation may be performed by theprocessor 240 (e.g., the image acquisition module 302). The plurality ofslice images may contain or include a portion of a myocardium of a leftventricle. In some embodiments, the plurality of slice images may be MRimages acquired by an MRI device or an MRI scanner. Alternatively oradditionally, the plurality of slice images may be obtained by processor240 (e.g., the image acquisition module 302). The slice images may becardiac cine MR images I_(NP) of a left ventricle at different timephases. The parameter “N” denotes an ordinal number of the slices; andthe parameter “P” denotes an ordinal number of the time phases. “N” and“P” may be integers. In some embodiments, “N” or “P” may be apredetermined value between one and thirty. For instance, “N” or “P” maybe five, ten, twenty, etc.

Merely by way of example, in 410, each of the slice images may beacquired as follows: First, a plurality of magnetic resonance (MR)signals of the slices regarding to heart (cardiac area) may be acquiredby the MR scanner. Merely by way of example, at each of a plurality oftime phases, a number of slices of the heart (e.g., left ventricle) areexcited and corresponding MR data lines are collected. The MR data linesmay be filled in K-Space, and the slice images (e.g., MR images) of theslices may be obtained using a Fourier transform. In addition, arelative short time window may be preset at each of the time phaseswhile the corresponding MR data lines are collected. Thus, motionartifacts caused by heart beat may be reduced, and quality of the sliceimages may be improved.

The left ventricle (with myocardium part) may be divided into severalslices along a longitudinal axis. The longitudinal axis may pass throughthe cardiac area from the basal part of the cardiac area to the apicalpart of the cardiac area. Corresponding slice images (MR images, e.g. asshown in FIG. 10A) may be represented by I_(NP). Furthermore, it may bepresumed that an M^(th) slice is located essentially at themid-ventricular (middle ventricular) position. “M” is an integer largerthan one.

In 420, an end-diastolic phase may be determined. In some embodiments,the determination may be performed by processor 240 (e.g., the timephase determination module 304). In some embodiments, the slice imagescorresponding to an end-diastolic phase (also referred to as sliceimages at the end-diastolic phase) may be determined by the dataprocessing system 130. FIG. 5 illustrates an exemplary process fordetermining the end-diastolic phase/period within a cardiac cycle.

In 430, a region of interest (ROI) in each of the slice images at theend-diastolic phase may be obtained. In some embodiments, the region ofinterest in each slice image may be determined. Merely by way ofexample, the region of interest (ROI) may have a shape of a square, atriangle, a rectangle, a parallelogram, a circle, etc. The ROI may havea center coinciding with the centroid of the largest roundnessconnection region (area “A”) as described in connection with FIG. 5. Thelength of the ROI may be longer than the length of a long axis of thelargest roundness connection region. As one example, the length of theROI may be longer than the length of the long axis of the largestroundness connection region (area “A”) by several pixels, e.g., betweenten and fifty pixels. In some embodiments, the length of the ROI may belonger than the length of the long axis of the largest connection region(area “A”) by five pixels, ten pixels, fifteen pixels, twenty pixels,twenty-five pixels, thirty pixels, thirty-five pixels, forty pixels,etc.

In 440, a blood pool region in the region of interest in each of theslice images at an end-diastolic phase may be segmented. In someembodiments, the segmentation of the blood pool region in the region ofinterest in each of the slice images at the end-diastolic phase may beperformed by processor 240 (e.g., image segmentation module 320). Insome embodiments, the segment of the blood pool region in 440 may berough, and may be refined further.

In some embodiments, the slice images I_(NP(ED)) at the end-diastolicphase/period may be segmented in an order from the mid-ventricular sliceimage at the end-diastolic phase to an apical slice image at theend-diastolic phase or to a basal slice image at the end-diastolicphase. Therefore, a blood pool region of the left ventricle at theend-diastolic phase may be segmented and delineated. The apical sliceimage at the end-diastolic phase may refer to an image of a slice at theapex of the heart at the end-diastolic phase. The basal slice image atthe end-diastolic phase may refer to an image of a slice at the base ofthe heart at the end-diastolic phase.

FIG. 6 illustrates an exemplary process for segmenting the blood poolregion in a mid-ventricular slice image at the end-diastolic phase

In 450, the region of interest with the (segmented) blood pool region ineach of the slice images at the end-diastolic phase may be transformedinto a polar coordinate image. The transformation may be performed bythe processor 240 (e.g., the image transformation module 318). The polarcoordinate images may include X-coordinates and Y-coordinates. TheX-coordinate may represent index numbers of radial lines originatingfrom the centroid of the segmented blood pool region. The Y-coordinatemay represent distances from points (pixels) on the radial lines to theorigin of the radial lines (or the centroid of the segmented blood poolregion). In some embodiments, the number of the radial lines may be anyvalue between one hundred and two hundred. As one example, the number ofthe radial lines may be one hundred and eighty. The start point ofscanning by a radial line may be an interior point inside the segmentedblood pool region, e.g., a number of pixels (e.g. 5-15 pixels) from anedge point of the segmented blood pool region. The end point of scanningby the radial line may be an exterior point outside of the segmentedblood pool region, e.g., a number of pixels (e.g., 15-45 pixels) fromthe edge point of the segmented blood pool region.

In 460, a dual dynamic programming operation may be performed on thepolar coordinate images to determine endocardial and epicardialboundaries in the slice image at the end-diastolic phase. Each radialline (each column of the polar coordinate image) may represent ascanning stage. Points (pixels) on the radial lines may representcandidate points at different scanning stages to determine theendocardial boundary or the epicardial boundary of the myocardium. Thedual dynamic programming operation may be performed on the polarcoordinate images to find an optimal path with a minimum accumulatedcost. Thus, the endocardium and epicardium of the myocardium of the leftventricle may be segmented accordingly.

The accumulated cost may include an internal cost (cost_(int)) and anexternal cost (cost_(ext)). The internal cost “cost_(int)” may representthe smoothness of two paths of the endocardial boundary and theepicardial boundary in the polar coordinate image. The external cost“cost_(ext)” may represent the gradient of the change of intensity ofpixels of two paths of the endocardial boundary and the epicardialboundary in the polar coordinate image.

The internal cost_(int) may be expressed as Equation 1:cos t _(int) =|y _(i) ^(endo) −y _(i-1) ^(endo) |/y _(range) +|y _(i)^(epi) −y _(i-1) ^(epi) |/y _(range) +|d _(i) ^(myo) −d _(i-1) ^(myo)|/d _(range).   (Equation 1)

The first term |y_(i) ^(endo)−y_(i-1) ^(endo)|/y_(range) of Equation 1may represent a normalized distance along a vertical direction of thecandidate points on the endocardial boundary (also referred to as theendocardial candidate points (pixels)) of adjacent columns (e.g., thei^(th) column, and the (i−1)^(th) column). The second term |y_(i)^(epi)−y_(i-1) ^(epi)|/y_(range) of Equation 1 may represent anormalized distance along a vertical direction of candidate points onthe epicardial boundary (also referred to herein as the epicardialcandidate points (pixels)) of adjacent columns (e.g., the i^(th) column,and the (i−1)^(th) column). The third term |d_(i) ^(myo)−d_(i-1)^(myo)|/d_(range) of Equation 1 may represent a normalized myocardialthickness determined by the endocardial candidate points (pixels) andepicardial candidate points (pixels) of adjacent columns (e.g., thei^(th) column, and the (i−1)^(th) column).

In Equation 1, y_(i) ^(endo) may denote vertical positions in the i^(th)column of the candidate points (pixels) on the endocardial boundary inthe polar coordinate image; y_(i-1) ^(endo) may denote verticalpositions in the (i−1)^(th) column of the candidate points (pixels) onthe endocardial boundary in the polar coordinate image; y_(i) ^(epi) maydenote vertical positions in the i^(th) column of the candidate points(pixels) on the epicardial boundary in the polar coordinate image;y_(i-1) ^(epi) may denote vertical positions in the (i−1)^(th) column ofthe candidate points (pixels) on the epicardial boundary in the polarcoordinate image; y_(range) may denote a maximum distance (e.g., threepixels) in two adjacent columns between two candidate points (pixels) onthe endocardial boundary in the polar coordinate image; d_(range) maydenote a maximum thickness of the myocardium (e.g., five pixels) in twoadjacent columns in the polar coordinate image.

The external cost_(ext) may be expressed as Equation 2:cos t _(ext) =−G _(i) ^(endo)/max G ^(endo) −G _(i) ^(epi)/max G^(epi).  (Equation 2)

In Equation 2, G_(i) ^(endo) may represent a gradient for the candidatepoints on the endocardial boundary (points or pixels on the i^(th)column) obtained from an original polar coordinate image; G_(i) ^(epi)may represent a gradient for the candidate points on the epicardialboundary (points or pixels on the i^(th) column) obtained from anenhanced polar coordinate image (e.g., obtained based on a statisticaldistribution of gray values of the myocardium in the slice images atdifferent time phases); and maxG^(endo) and maxG^(epi) may denote themaximum gradient values for the original and the enhanced polarcoordinate images, respectively.

In 470, the polar coordinate image including the determined endocardialand epicardial boundaries may be transformed into a Cartesian coordinateimage, a slice image at the end-diastolic phase in which the endocardiumand the epicardium are segmented. The transformation may be performed bythe processor 240 (e.g., the image transformation module 318).

It should be noted that process 400 described above is provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. Apparently for persons having ordinary skills in theart, numerous variations and modifications may be conducted under theteaching of the present disclosure. However, those variations andmodifications do not depart the protection scope of the presentdisclosure. In some embodiments, some operations may be optional. Forexample, 420, 430, and/or 440 may be omitted.

FIG. 5 illustrates a flowchart of an exemplary process 500 fordetermining the end-diastolic phase according to some embodiments of thepresent disclosure. In 510, a number of mid-ventricular slice images(I_(MP)) at a plurality of time phases in a cardiac cycle may beobtained. In some embodiments, the operation may be performed by theprocessor 240 (e.g., the image acquisition module 302).

In 520, an initial region of interest (ROI₀) may be determined in eachof the mid-ventricular slice images I_(MP). The ROI₀ of amid-ventricular slice images I_(MP) may have a center point coincidingwith a center point of the mid-ventricular slice image I_(MP). The ROI₀may have a radius of several pixels. For example, a dimension of theradius may be between sixty and one hundred and twenty pixels. As oneexample, the dimension of the radius may be one hundred pixels. In someembodiments, the operation may be performed by the processor 240 (e.g.,the time phase determination module 304).

In 530, a maximum intensity projection (MIP) image of the ROI₀ acrossthe plurality of time phases in the cardiac cycle may be determined.

In 540, a clustering operation may be performed on the MIP image toobtain one or more projection clusters. The clustering operation may bebased on, for example, a Fuzzy c-mean clustering algorithm. Theclustering operation may be performed by the processor 240 (e.g., thecluster module 308).

In 550, one or more connection regions may be identified based on theprojection clusters obtained in 540 (e.g., 1010, 1020, 1030, 1040, or1050, as shown in FIG. 10C). Further, the roundness of each of theconnection regions may be determined. The identification of theconnection regions may be performed in the processor 240. Then, theconnection region with the largest roundness (also referred to herein asa largest roundness connection region) may be identified from the one ormore connection regions. The roundness may be defined by followingformula: 4πS/Q², in which “S” is the area of the connection region, and“Q” is the perimeter of the connection region in a binary image obtainedby performing the clustering method on the MIP image. In someembodiments, the MIP image after the clustering operation may include anumber of clusters between two and ten, e.g., two clusters, threeclusters, and four clusters, etc.

In 560, a mean gray value of a portion of each of the mid-ventricularslice images I_(MP) at different time phases corresponding to thelargest roundness connection region (area “A”) may be determined. Thedetermination of the mean gray value may be performed by the processor240 (e.g., the gray value determination module 306).

In 570, the mid-ventricular slice image having the maximum mean grayvalue may be determined as the mid-ventricular slice image I_(MP(ED)) atan end-diastolic phase/period (also referred to herein as the firstslice image at the end-diastolic phase). The time phase corresponding tothe mid-ventricular slice image with the maximum mean gray value may bedetermined as the end-diastolic phase/period. The determination of theend-diastolic phase/period and the determination of the mid-ventricularslice image at the end-diastolic phase may be performed by the processor240 (e.g., the time phase determination module 304).

FIG. 6 illustrates a flowchart of an exemplary process 600 forsegmenting a blood pool region according to some embodiments of thepresent disclosure. In 610, the mid-ventricular slice image I_(MP(ED))at the end-diastolic phase (or the region of interest of I_(MP(ED))) maybe corrected based on a correction algorithm including, e.g., Gammacorrection. The corrected slice image I_(MP(ED))′ may be obtainedaccordingly. The Gamma correction may be performed to enhance thecontrast of the blood pool region and the myocardium. It may alsofacilitate subsequent processing including, for example, identificationand segmentation. The correction may be performed by the processor 240(e.g., the image correction module 310).

In 620, a clustering operation may be performed on the corrected imageI_(MP(ED))′ to identify clusters of areas. The operation may beperformed by the processor 240 (e.g., the cluster module 308). Theclustering operation may be based on, for example, a Fuzzy c-meansclustering algorithm (FCM). In some embodiments, the clusters of areasmay include a number of clusters, e.g., between one and ten clusters.Merely by way of example, the number of the clusters may include two.

In 630, the brightness of each of the clusters may be determined. In640, a cluster having a highest brightness may be identified. Then in650, areas belonging the cluster having the highest brightness may bedetermined as candidate areas S_(M).

In 660, for each of the candidate areas S_(M), an overlapping area wherethe candidate area overlaps the largest roundness connection region(area “A”) may be identified. Further, a candidate area S_(M) with thelargest overlapping area with the largest roundness connection region(area “A”) may be designated as the blood pool region in themid-ventricular slice image at the end-diastolic phase. “M” denotes theordinal of the mid-ventricular slice image. The blood pool region in themid-ventricular slice image at the end-diastolic phase may thus besegmented. The segmentation of the blood pool region may be performed bythe processor 240 (e.g., the image segmentation module 320).

In 670, blood pool regions in other slice images at the end-diastolicphase (also referred to herein as second slice images at theend-diastolic phase) than a mid-ventricular slice image at theend-diastolic phase may be segmented based on the segmentation of theblood pool region in the mid-ventricular slice image at theend-diastolic phase (the first slice image at the end-diastolic phase).In some embodiments, the operation may be performed by the processor 240(e.g., the image segmentation module 302). In some embodiments, thesegmented blood pool region in the mid-ventricular slice image at theend-diastolic phase may be used as guidance to segment the blood poolregions in the second slice images at the end-diastolic phase. Forexample, blood pool regions in the 1^(st) slice image through the(M−1)^(th) slice image at the end-diastolic phase may be segmentedaccordingly. Blood pool regions in the (M+1)^(th) slice image throughthe N^(th) slice image at the end-diastolic phase may be segmentedaccordingly. A segmented blood pool region in a slice image at theend-diastolic phase may be utilized as guidance for segmenting the bloodpool region in a slice image at the end-diastolic phase.

Merely by way of example, the M^(th) slice image may be designated asthe prior slice image. The (M−1)^(th) slice image or the (M+1)^(th)slice image may be designated as the current slice image. The (M−1)^(th)slice image or the (M+1)^(th) slice image may be regarded as a priorslice image with respect to the (M−2)^(th) slice image or the (M+2)^(th)slice image, and so on.

Merely by way of example, firstly, a blood pool region of the M^(th)slice image at the end-diastolic phase may be segmented, and a bloodpool region of the (M−1)^(th) slice image at the end-diastolic phase maybe segmented based on the segmented blood pool region in the M^(th)slice image at the end-diastolic phase. Then, a blood pool region of the(M−2)^(th) slice image at the end-diastolic phase may be segmented basedon the segmented blood pool region in the (M−1)^(th) slice image at theend-diastolic phase. The process may be repeated. In addition, a bloodpool region of the (M+1)^(th) slice image at the end-diastolic phase maybe segmented based on the segmented blood pool region in the M^(th)slice image at the end-diastolic phase. A blood pool region in the(M+2)^(th) slice image at the end-diastolic phase may be segmented basedon the segmented blood pool region in the (M+1)^(th) slice image at theend-diastolic phase. The process may be repeated.

Merely by way of example, the blood pool regions in the second sliceimages at the end-diastolic phase may be segmented as follows.

The (K−1)^(th) slice image at the end-diastolic phase I_((K−1)P(ED)) orthe (K+1)^(th) slice image at the end-diastolic phase I_((K+1)P(ED)) maybe regarded as a current slice image, and the K^(th) slice image at theend-diastolic phase I_(KP(ED)) may be regarded as a prior slice image. Ablood pool region in the prior slice image (the K^(th) slice image) atthe end-diastolic phase may be segmented. A blood pool region in thecurrent slice image (the (K−1)^(th) slice image or the (K+1)^(th) sliceimage) at the end-diastolic phase may be segmented based on thesegmented blood pool region in the current slice image.

Furthermore, a region of interest (ROI) in the current slice imageI_((K−1)P(ED)) or I_((K+1)P(ED)) may have a center (centroid) coincidingwith the centroid of the segmented blood pool region in the prior sliceimage I_(KP(ED)). In some embodiments, the ROI of the current sliceimage may have a shape of a square, a triangle, a rectangle, aparallelogram, a circle, etc. The length of the shape may refer to thelongest dimension of the shape. The length of the ROI may be longer thanthe length of the long axis of the prior slice image. As one example,the length of the ROI may be longer than the length of the long axis ofthe prior slice image by several pixels, e.g., between ten pixels andfifty pixels. In some embodiments, the length of the ROI may be longerthan the length of the long axis of the prior slice image by fivepixels, ten pixels, fifteen pixels, twenty pixels, twenty-five pixels,thirty, thirty-five pixels, forty pixels, etc. In some embodiments, theROI of the current slice image I_((K−1)P(ED)) or I_((K+1)P(ED)) at theend-diastolic phase may be further processed based on Gamma correction.A corrected image I_((K−1)P(ED))′ or I_((K+1)P(ED))′ may be obtainedaccordingly.

The Fuzzy c-means clustering (FCM) algorithm may then be applied to thecorrected images I_((K+1)P(ED))′ or I_((K−1)P(ED))′ to obtain clustersof areas. Merely by way of example, the clusters of areas may include anumber of clusters, e.g., between two and ten clusters. In someembodiments, the number of the clusters may include two, three, four,five, etc. The brightness of each of the clusters may be determined. Acluster having the highest brightness may then be identified. Areasbelonging the cluster having the highest brightness may be designated ascandidate areas S_((k+1)) or S_((k−1)).

The candidate areas S_((K−1)) or S_((K+1)) with the largest overlappingarea with the segmented blood pool region in the prior slice imageI_(KP(ED)) may be designated as the segmented blood pool region in thecurrent slice image at the end-diastolic phase. “K” may denote anordinal number of the slice images. K may be equal to M, larger than M,or smaller than M.

Furthermore, the length of the long axis of the segmented blood poolregion in the current slice image I_((K−1)P(ED)) or I_((K+1)P(ED)) maybe compared with the length of the long axis of the segmented blood poolregion of the prior slice image I_(KP(ED)). In some embodiments, if adifference (e.g., ratio) therebetween is larger than a threshold (e.g.,1.2 times), it may be deemed that a left ventricular outflow tract(LVOT) occurs. In some embodiments, a length-width ratio of thesegmented blood pool region in the current slice image may be comparedwith a length-width ratio of the segmented blood pool region in theprior slice image. If a difference (e.g., ratio) therebetween is largerthan a threshold (e.g., 1.3 times), it may be deemed that a LVOT occurs.The current slice may be a slice before the mid-ventricular slice (e.g.,one slice closer to the basal slice than the mid-ventricular slice).

Furthermore, if the LVOT occurs, parameters used on the Gamma correctionmay be adjusted. Then, the ROI of the current slice image may beprocessed by the adjusted Gamma correction again to obtain a correctedimage. The Fuzzy c-means clustering (FCM) algorithm may then be employedto segment the blood pool region of the corrected image.

Furthermore, if the LVOT still occurs after the aforementionedprocessing, a ray-scanning approach may be employed to remove anover-segmented portion of the segmented blood pool region in the currentslice image. The over-segmented portion may refer to a region that maynot belong to a blood pool region.

FIG. 7 illustrates an exemplary process 700 for segmenting a blood poolregion based on the ray-scanning approach.

In 710, a centroid (e.g., point “a” in FIG. 11A) of the segmented bloodpool region with an over-segmented portion of a current slice image maybe determined as an origin.

In 720, ray-scanning may be performed on the blood pool region with theover-segmented portion from the origin along a plurality of radiallines. The plurality of radial lines may be along different directions(angles) from the origin.

In 730, edge points (pixels) of the segmented blood pool region with theover-segmented portion in the current slice image may be extracted. Edgepoint(s) at each radial line with the shortest radial distances to theorigin may also be identified, e.g. points b1, b2, and b3 in FIG. 11A.The extraction and identification of the edge points may be performed bythe processor 240 (e.g., the point processing module 312).

In 740, a mean value and a standard deviation of the radial distances ofthe identified edge points to the origin may be respectively determined.It should be noted that another average value of the radial distance,e.g., a weighted mean value, etc., may be used.

In 750, the identified edge point(s) may be filtered by removing edgepoint(s) whose radial distances is/are larger than the sum of the meanvalue and the standard deviation to obtain remaining or filtered edgepoints. For example, the edge point(s) (e.g., b3 in FIG. 11A) to theorigin with a radial distance larger than a sum of the mean value andthe standard deviation is removed, and edge points (e.g. b1, b2) areretained. The operation may be performed by the processor 240 (e.g., thepoint processing module 312).

In 760, a closed curve may be generated based on the remaining edgepoints. In some embodiments, the remaining edge points may beinterpolated and fitted to obtain the closed curve. The operation may beperformed by the processor 240 (e.g., the boundary determination module314).

In 770, a corresponding region in the closed curve may be determined asa segmented blood pool region of the current slice image. The operationmay be performed by the processor 240 (e.g., image segmentation module320).

FIG. 8 illustrates a flowchart of an exemplary process 800 forsegmenting the epicardium and the endocardium of a left ventricle inaccordance with some embodiments of the present disclosure. In someembodiments, at least part of process 800 may be performed by processor240. The process 800 may include the following operations.

In 810, a plurality of slice images (e.g. MR images) of a myocardium ofa left ventricle at a plurality of time phases in a cardiac cycle may beobtained.

In 820, an end-diastolic phase may be determined from the plurality oftime phases.

In 830, a region of interest in each of the slice images at theend-diastolic phase may be obtained.

In 840, a blood pool region in the region of interest in each of theslice images at the end-diastolic phase may be segmented.

In 850, the region of interest with the (segmented) blood pool region ineach of the slice image may be transformed into a polar coordinateimage.

In 860, a dual dynamic programming operation may be performed on thepolar coordinate image to determine endocardial and epicardialboundaries of the myocardium in the each of the polar coordinate imagesat the end-diastolic phase.

In 870, endocardial and epicardial boundaries of the myocardium in theslice images at the time phases other than the end-diastolic phase maybe determined, based on the endocardial and epicardial boundaries of themyocardium in the polar coordinate images at the end-diastolic phase.The endocardium and epicardium of the myocardium in the mid-ventricularslice image at the end-diastolic phase may thus be segmented. Thesegmentation may be performed by the processor 240 (e.g., the imagesegmentation module 320).

In 880, the polar coordinate image(s) including endocardial andepicardial boundaries may be transformed into Cartesian coordinateimage(s) to obtain the endocardial and epicardial boundaries of themyocardium in each of the slice images. In some embodiments, theendocardial and epicardial boundaries of the myocardium of the leftventricle (LV) may further be convex-hulled and/or smoothed to obtainthe endocardium and epicardium of the myocardium of the left ventricle.The operation may be performed by the processor 240.

Operations 810˜860 may be respectively performed similarly to 410˜460,and 880 may be performed similarly to 470. FIG. 9 illustrates anexemplary process for 870.

It should be noted that process 800 described above is provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. Apparently for persons having ordinary skills in theart, numerous variations and modifications may be conducted under theteaching of the present disclosure. However, those variations andmodifications do not depart the protection scope of the presentdisclosure. In some embodiments, some operations may be optional. Forexample, 820, 830, and/or 840 may be omitted. In some embodiments, theorder of some operations may be changed. For example, 870 may beperformed before 860. Similar modifications should fall within the scopeof the present disclosure.

FIG. 9 illustrates a flowchart of exemplary process 900 for segmentingthe endocardium and the epicardium of a myocardium of a left ventriclein the slice images at the time phases other than the end-diastolicphase according to some embodiments of the present disclosure. In 910, aK^(th) slice image at a L^(th) time phase may be regarded as a current(time phase) slice image I_(KL), and a K^(th) slice image at a(L−1)^(th) time phase may be regarded as a prior (time phase) sliceimage I_(K(L-1)). The endocardial and epicardial boundaries of the polarcoordinate image at the prior time phase may be determined as describedelsewhere in the present disclosure. The centroid of the blood poolregion in the prior slice image may be determined as an origin of theray scanning for the current (time phase) slice image I_(KL). Theoperation may be performed by the processor 240 (e.g., the imageacquisition module 302).

In 920, the current slice image In, may be transformed into a polarcoordinate image P_(KL), which may be disposed near the endocardial andepicardial boundaries of the polar coordinate image already segmentedfrom the prior slice image I_(K(L-1)). The operation may be performed bythe processor 240 (e.g., the image transformation module 318).

In 930, a gray value distribution of the slice images of the myocardiumfrom the end-diastolic phase to the prior time phase ((L−1)^(th) timephase) in a same cardiac cycle may be determined. The operation may bedetermined by the processor 240 (e.g., the gray value determinationmodule 306).

In 940, the current (time phase) slice image In, may be enhanced. Insome embodiments, a gray value of a pixel in the K^(th) slice image (thecurrent slice image) at the L^(th) time phase may be multiplied by thevalue of the gray value distribution of the corresponding pixel toobtain an enhanced current (time phase) slice image. The operation maybe performed by the processor 240 (e.g., the image enhancement module316).

In 950, the enhanced current (time phase) slice image (enhanced I_(KL))of the myocardium may be transformed into a polar coordinate imageP_(card). The operation may be performed by the processor 240 (e.g., theimage transformation module 318).

In 960, a dual dynamic programming operation may be performed on thepolar coordinate images P_(KL) and P_(card) together to find optimalcurves of the endocardial and epicardial boundaries of the myocardium ofthe left ventricle (LV) in both polar coordinate images P_(KL), andP_(card), respectively. The operation may be performed by the processor240 (e.g., the image segmentation module 320). The searching of theendocardial and epicardial boundaries within the polar coordinate imagesP_(KL), and P_(card) at the current time phase (the L^(th) time phase)may be performed in a region restricted by the endocardial andepicardial boundaries at the prior time phase (the (L−1)^(th) timephase).

In some embodiments, the endocardial and epicardial boundaries in thecurrent and/or prior slice image of the myocardium of the left ventricle(LV) may further be convex-hulled and/or smoothed to obtain theendocardium and epicardium of the myocardium of the left ventricle.

Merely by way of example, the searching of the endocardial andepicardial boundaries of the slice images at the current time phase maybe performed in a region constrained between twenty pixels above theendocardial boundaries and ten pixels below the epicardial boundaries ofthe slice images at the prior time phase.

It should be noted that process 900 described above is provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. Apparently for persons having ordinary skills in theart, numerous variations and modifications may be conducted under theteaching of the present disclosure. However, those variations andmodifications do not depart the protection scope of the presentdisclosure. In some embodiments, some operations may be optional. Forexample, 940 may be omitted. Similar modifications should fall withinthe scope of the present disclosure.

EXAMPLES

The following examples are provided for illustration purposes, and notintended to limit the scope of the present disclosure.

Example 1

FIG. 10A illustrates a mid-ventricular slice image of a part of amyocardium of a left ventricle at one time phase. The slice imageincludes an initial region of interest (ROI). The ROI includes a bloodpool region. Then the maximum intensity projection image of the ROI wasobtained as shown in FIG. 10B. A clustering operation based on theFuzzy-c mean clustering algorithm was performed on the maximum intensityprojection image of the ROI, and a binary image was obtained as shown inFIG. 10C. In the binary image, several connection regions wereidentified, e.g., 1010, 1020, 1030, 1040, 1050, etc. The roundness ofeach connection region was determined. The connection region with thelargest roundness (1020 in FIG. 10C) was determined as the largestroundness connection region (area “A”). Based on the largest roundnessconnection region (1020 in FIG. 10C), the blood pool region on the MIPimage of the mid-ventricle slice image was determined as shown in FIG.10D.

Example 2

FIG. 11A through FIG. 11C illustrate exemplary diagrams of segmentationof a blood pool region in a slice image when the LVOT occurs. As shownin FIG. 11A, a centroid (e.g. point “a”) of the segmented blood poolregion with an over-segmented portion 1110 of a slice image wasdetermined as an origin. Then, ray-scanning was performed on the bloodpool region with the over-segmented portion 1110 from the origin along aplurality of radial lines. The plurality of radial lines were alongdifferent directions (angles) from the origin. Edge points (pixels) ofthe segmented blood pool region with the over-segmented portion 1110 inthe slice image was extracted. Edge point(s) at each radial line withthe shortest radial distance to the origin was also selected, e.g.,points b1, b2, or b3 in FIG. 11A.

Then, a mean value and a standard deviation of the radial distances ofthe selected edge points to the origin was respectively determined. Edgepoint(s) with radial distances larger than the sum of the mean value andthe standard deviation were removed to obtain remaining edge points. Forexample, the edge point(s) (e.g. b3) to the origin with a radialdistance larger than the sum of the mean value and the standarddeviation was removed, and edge points (e.g. b1, b2) were retained.

Then, a closed curve (the curve “d” in FIG. 11C) was generated based onthe remaining edge points through interpolating and fitting of theremaining edge points. The region in the closed curve was determined asthe segmented blood pool region of the slice image.

Example 3

FIG. 12A through FIG. 12O illustrate segmentation results of theepicardium and endocardium of the myocardium of a left ventricle inthree representative slice images (a slice image-1, a slice image-2, aslice image-3) at five time phases (a time phase-1, a time phase-2, atime phase-3, a time phase-4, and a time phase-5). As used herein, theslice image-1 refers to an image at a first slice in terms of location;the slice image-2 refers to an image at a second slice in terms oflocation; the slice image-3 refers to an image at a third slice in termsof location.

FIG. 12A through FIG. 12E illustrate the segmentation results of theepicardium and endocardium of the myocardium of the left ventricle inthe slice image-1 at the five time phases. FIG. 12F through FIG. 12Jillustrate the segmentation results of the epicardium and endocardium ofthe myocardium of the left ventricle in the slice image-2 at the fivetime phases. FIG. 12L through FIG. 12O illustrate the segmentationresults of the epicardium and endocardium of the myocardium of the leftventricle in the slice image-3 at the five time phases. The segmentationresults shown in FIG. 12A through FIG. 12O were obtained through theprocesses illustrated in FIG. 4 through FIG. 9. As shown in FIG. 12Athrough FIG. 12O, the epicardium and endocardium of the myocardium ineach of the slice images at each of the time phases can be recognized.

Example 4

In this study, 9 clinical short axis (SA) cardiac cine MR images wereobtained from Ruijin Hospital of Shanghai Jiaotong University to developand evaluate an automated scheme. All MR images were acquired under one1.5-T MR scanner. Each cardiac cine MR images included 10-15 slices(sections) for the coverage of LV, and 20 time phases throughout acardiac cycle. The pixel size in x, y axis was 0.8-0.9 mm with thematrix size of 512×512, and the pixel size in z axis was 8 mm. Typicalparameters of TR, TE, and flip angle were 4.29 ms, 1.86 ms, and 45degrees, respectively. The segmentation performance of 2020 slice imageswas subjectively evaluated in corresponding dataset by use of threescores. A score “3” may indicate a good result that (almost) does notneed manual revision; a score “2” may indicate an acceptable result thatneeds minor manual revision; and a score “1” may indicate a poorsegmentation result that may not be used in clinical practice. Table 1shows the number and the percentage of the evaluated slices with respectto the three scores. The results show that based on the method disclosedherein, 90.5% of 2020 slice images were segmented automatically with thescore of 2 or 3, for which none or minor manual revision was needed.

TABLE 1 Segmentation performance by subjective evaluation with respectto the three scores Score “1” Score “2” Score “3” Number 192 243 1585Percentage 9.5% 12% 78.5%

Furthermore, the segmentation result of the endocardium and epicardiummay be used for quantitative analysis of global and regionalmeasurements, such as ventricle volumes, ejection fraction, and wallthickness, which could help the radiologists improve the accuracy andefficiency of heart function evaluation.

Having thus described the basic concepts, it may be rather apparent tothose skilled in the art after reading this detailed disclosure that theforegoing detailed disclosure is intended to be presented by way ofexample only and is not limiting. Various alterations, improvements, andmodifications may occur and are intended to those skilled in the art,though not expressly stated herein. These alterations, improvements, andmodifications are intended to be suggested by this disclosure, and arewithin the spirit and scope of the exemplary embodiments of thisdisclosure.

Moreover, certain terminology has been used to describe embodiments ofthe present disclosure. For example, the terms “one embodiment,” “anembodiment,” and/or “some embodiments” mean that a particular feature,structure or characteristic described in connection with the embodimentis included in at least one embodiment of the present disclosure.Therefore, it is emphasized and should be appreciated that two or morereferences to “an embodiment” or “one embodiment” or “an alternativeembodiment” in various portions of this specification are notnecessarily all referring to the same embodiment. Furthermore, theparticular features, structures or characteristics may be combined assuitable in one or more embodiments of the present disclosure.

Further, it will be appreciated by one skilled in the art, aspects ofthe present disclosure may be illustrated and described herein in any ofa number of patentable classes or context including any new and usefulprocess, machine, manufacture, or composition of matter, or any new anduseful improvement thereof. Accordingly, aspects of the presentdisclosure may be implemented entirely hardware, entirely software(including firmware, resident software, micro-code, etc.) or combiningsoftware and hardware implementation that may all generally be referredto herein as a “block,” “module,” “engine,” “unit,” “component,” or“system.” Furthermore, aspects of the present disclosure may take theform of a computer program product embodied in one or more computerreadable media having computer readable program code embodied thereon.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including electro-magnetic, optical, or thelike, or any suitable combination thereof. A computer readable signalmedium may be any computer readable medium that is not a computerreadable storage medium and that may communicate, propagate, ortransport a program for use by or in connection with an instructionexecution system, apparatus, or device. Program code embodied on acomputer readable signal medium may be transmitted using any appropriatemedium, including wireless, wireline, optical fiber cable, RF, or thelike, or any suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB. NET,Python or the like, conventional procedural programming languages, suchas the “C” programming language, Visual Basic, Fortran 2003, Perl, COBOL2002, PRP, ABAP, dynamic programming languages such as Python, Ruby andGroovy, or other programming languages. The program code may executeentirely on the operator's computer, partly on the operator's computer,as a stand-alone software package, partly on the operator's computer andpartly on a remote computer or entirely on the remote computer orserver. In the latter scenario, the remote computer may be connected tothe operator's computer through any type of network, including a localarea network (LAN) or a wide area network (WAN), or the connection maybe made to an external computer (for example, through the Internet usingan Internet Service Provider) or in a cloud computing environment oroffered as a service such as a Software as a Service (SaaS).

Furthermore, the recited order of processing elements or sequences, orthe use of numbers, letters, or other designations therefore, is notintended to limit the claimed processes and methods to any order exceptas may be specified in the claims. Although the above disclosurediscusses through various examples what is currently considered to be avariety of useful embodiments of the disclosure, it is to be understoodthat such detail is solely for that purpose, and that the appendedclaims are not limited to the disclosed embodiments, but, on thecontrary, are intended to cover modifications and equivalentarrangements that are within the spirit and scope of the disclosedembodiments. For example, although the implementation of variouscomponents described above may be embodied in a hardware device, it mayalso be implemented as a software only solution—e.g., an installation onan existing server or mobile device.

Similarly, it should be appreciated that in the foregoing description ofembodiments of the present disclosure, various features are sometimesgrouped together in a single embodiment, figure, or description thereoffor the purpose of streamlining the disclosure aiding in theunderstanding of one or more of the various inventive embodiments. Thismethod of disclosure, however, is not to be interpreted as reflecting anintention that the claimed subject matter requires more features thanare expressly recited in each claim. Rather, inventive embodiments liein less than all features of a single foregoing disclosed embodiment.

In some embodiments, the numbers expressing quantities of ingredients,properties, and so forth, used to describe and claim certain embodimentsof the application are to be understood as being modified in someinstances by the term “about,” “approximate,” or “substantially.” Forexample, “about,” “approximate,” or “substantially” may indicate ±20%variation of the value it describes, unless otherwise stated.Accordingly, in some embodiments, the numerical parameters set forth inthe written description and attached claims are approximations that mayvary depending upon the desired properties sought to be obtained by aparticular embodiment. In some embodiments, the numerical parametersshould be construed in light of the number of reported significantdigits and by applying ordinary rounding techniques. Notwithstandingthat the numerical ranges and parameters setting forth the broad scopeof some embodiments of the application are approximations, the numericalvalues set forth in the specific examples are reported as precisely aspracticable.

Each of the patents, patent applications, publications of patentapplications, and other material, such as articles, books,specifications, publications, documents, things, and/or the like,referenced herein is hereby incorporated herein by this reference in itsentirety for all purposes, excepting any prosecution file historyassociated with same, any of same that is inconsistent with or inconflict with the present document, or any of same that may have alimiting affect as to the broadest scope of the claims now or laterassociated with the present document. By way of example, should there beany inconsistency or conflict between the descriptions, definition,and/or the use of a term associated with any of the incorporatedmaterial and that associated with the present document, the description,definition, and/or the use of the term in the present document shallprevail.

In closing, it is to be understood that the embodiments of theapplication disclosed herein are illustrative of the principles of theembodiments of the application. Other modifications that may be employedmay be within the scope of the application. Thus, by way of example, butnot of limitation, alternative configurations of the embodiments of theapplication may be utilized in accordance with the teachings herein.Accordingly, embodiments of the present application are not limited tothat precisely as shown and described.

We claim:
 1. A method for cardiac image segmentation, implemented on atleast one machine each of which has at least one processor and storage,the method comprising: obtaining a plurality of slice images of amyocardium of a left ventricle at a plurality of time phases in acardiac cycle; determining, based on at least one of the plurality ofslice images, an end-diastolic phase from the plurality of time phases;retrieving, from the plurality of slice images, a first slice image atthe end-diastolic phase and a second slice image at the end-diastolicphase, wherein the first slice image at the end-diastolic phase includesa mid-ventricular slice image at the end-diastolic phase and the secondslice image at the end-diastolic phase is different from the first sliceimage at the end-diastolic phase; obtaining a region of interest in thefirst slice image at the end-diastolic phase and a region of interest inthe second slice image at the end-diastolic phase; segmenting a bloodpool region in the region of interest in the first slice image at theend-diastolic phase; using the segmentation of the blood pool region inthe region of interest in the first slice image at the end-diastolicphase as guidance for segmenting a blood pool region in the region ofinterest in the second slice image at the end-diastolic phase;transforming the region of interest in the first slice image at theend-diastolic phase into a polar coordinate image; performing a dualdynamic programming operation on the polar coordinate image to determineendocardial and epicardial boundaries of the myocardium in the polarcoordinate image by selecting points with a minimum accumulated cost inthe polar coordinate image to find optimal paths representing theendocardial and epicardial boundaries of the myocardium in the polarcoordinate image; and transforming the polar coordinate image into aCartesian coordinate image to obtain the endocardial and epicardialboundaries of the myocardium in the first slice image at theend-diastolic phase.
 2. The method of claim 1, wherein the plurality ofslice images are acquired from scanning of the left ventricle at theplurality of time phases.
 3. The method of claim 1, wherein thedetermining an end-diastolic phase from the plurality of time phasescomprises: selecting mid-ventricular slice images at the plurality oftime phases; determining an initial region of interest in each of themid-ventricular slice images; obtaining a maximum intensity projectionimage of the initial region of interest for the mid-ventricular sliceimages; performing a clustering operation on the maximum intensityprojection image to obtain projection clusters; identifying, based onthe clustering operation, connection regions from the projectionclusters; determining roundness of each of the connection regions;identifying, from the connection regions, a connection region with thelargest roundness; determining a mean gray value of each of themid-ventricular slice images in the connected region with the largestroundness; and determining the end-diastolic phase, based on a timephase corresponding to one of the mid-ventricular slice images with amaximum mean gray value.
 4. The method of claim 3, wherein a center ofthe region of interest coincides with a centroid of the connectionregion with the largest roundness.
 5. The method of claim 3, wherein alength of the region of interest is longer than the length of a longaxis of the connection region with the largest roundness.
 6. The methodof claim 1, further comprising: performing Gamma correction on theregion of interest in the mid-ventricular slice image at theend-diastolic phase to obtain a corrected image.
 7. The method of claim6, further comprising: performing a Fuzzy c-means clustering operationon the corrected image to obtain clusters of areas; determiningbrightness of each of the clusters; identifying, among the clusters ofareas, a cluster having the highest brightness; and designating areasbelonging the cluster having the highest brightness as candidate areas.8. The method of claim 7, wherein the segmenting a blood pool region inthe region of interest comprises: for each of the candidate areas,determining an overlapping area where the candidate area overlaps theconnection region with the largest roundness; and designating thecandidate area with the largest overlapping area among the determinedoverlapping areas as the blood pool region in the mid-ventricular sliceimage at the end-diastolic phase.
 9. The method of claim 1, wherein thesegmenting a blood pool region in the region of interest in themid-ventricular slice image at the end-diastolic phase and thesegmenting a blood pool region in the region of interest of the secondslice image at the end-diastolic phase are performed in an order fromthe mid-ventricular slice image at the end-diastolic phase to an apicalslice image at the end-diastolic phase.
 10. The method of claim 1,wherein the segmenting a blood pool region in the region of interest ofthe mid-ventricular slice image at the end-diastolic phase and thesegmenting a blood pool region in the region of interest in the secondslice image at the end-diastolic phase are performed in an order fromthe mid-ventricular slice image at the end-diastolic phase to a basalslice image at the end-diastolic phase.
 11. A method for cardiac imagesegmentation, implemented on at least one machine each of which has atleast one processor and storage, the method comprising: obtaining aplurality of slice images of a myocardium of a left ventricle at aplurality of time phases in a cardiac cycle, the plurality of timephases including a first time phase of an end-diastolic phase and asecond time phase different from the first time phase; determining,based on at least one of the plurality of slice images, theend-diastolic phase; retrieving, from the plurality of slice images, aslice image corresponding to the first time phase and a slice imagecorresponding to the second time phase; obtaining a first region ofinterest in the slice image corresponding to the first time phase;segmenting a first blood pool region in the first region of interest,wherein the segmentation of the first blood pool region is from amid-ventricular slice image at the first time phase to a slice image atthe first time phase other than the mid-ventricular slice image at thefirst time phase; transforming the first region of interest into a firstpolar coordinate image; determining, based on a dual dynamic programmingoperation, endocardial and epicardial boundaries of the myocardium inthe first polar coordinate image by selecting points with a minimumaccumulated cost in the first polar coordinate image to find optimalpaths representing the endocardial and epicardial boundaries of themyocardium in the first polar coordinate image; obtaining a secondregion of interest in the slice image corresponding to the second timephase; transforming the second region of interest into a second polarcoordinate image; determining endocardial and epicardial boundaries ofthe myocardium in the second polar coordinate image based on thesegmentation of the endocardial and epicardial boundaries of themyocardium in the first polar coordinate image; and transforming thefirst polar coordinate image and the second polar coordinate image intoCartesian coordinate images to obtain the endocardial and epicardialboundaries of the myocardium in the slice image corresponding to thefirst time phase and the slice image corresponding to the second timephase.
 12. The method of claim 11 further comprising: convex-hulling andsmoothing the endocardial and epicardial boundaries of the myocardium inthe slice image corresponding to the first time phase or the slice imagecorresponding to the second time phase.
 13. The method of claim 11,wherein the obtaining a plurality of slice images of a myocardium of theleft ventricle at a plurality of time phases comprises: obtainingmid-ventricular slice images at the plurality of time phases.
 14. Themethod of claim 13, wherein the obtaining a plurality of slice images ofa myocardium of the left ventricle comprises: obtaining a firstmid-ventricular slice image at the first time phase, a secondmid-ventricular slice image at the second time phase, and a thirdmid-ventricular slice image at a third time phase, wherein the firsttime phase, the second time phase, and the third time phase are at ananti-chronological order in the plurality of time phases.
 15. The methodof claim 14 further comprising obtaining a third region of interest inthe third mid-ventricular slice image; obtaining a fourth region ofinterest in the first mid-ventricular slice image; obtaining a fifthregion of interest in the second mid-ventricular slice image;transforming the third region of interest into a third polar coordinateimage; transforming the fourth region of interest into a fourth polarcoordinate image; transforming the fifth region of interest into a fifthpolar coordinate image; determining endocardial and epicardialboundaries of the myocardium in the fourth polar coordinate image;determining endocardial and epicardial boundaries of the myocardium inthe fifth polar coordinate image; and determining endocardial andepicardial boundaries of the myocardium in the third polar coordinateimage based on the determination of the endocardial and epicardialboundaries of the myocardium in the fourth polar coordinate image andthe fifth polar coordinate image.
 16. A system comprising: at least oneprocessor, and instructions that, when executed by the at least oneprocessor, cause the at least one processor to effectuate a methodcomprising: obtaining a plurality of slices images of a myocardium of aleft ventricle at a plurality of time phases in a cardiac cycle;determining, based on at least one of the plurality of slice images, anend-diastolic phase from the plurality of time phases; retrieving, fromthe plurality of slice images, a first slice image at the end-diastolicphase and a second slice image at the end-diastolic phase, wherein thefirst slice image at the end-diastolic phase includes a mid-ventricularslice image at the end-diastolic phase and the second slice image at theend-diastolic phase is different from the first slice image at theend-diastolic phase; obtaining a region of interest in the first sliceimage at the end-diastolic phase and a region of interest in the secondslice image at the end-diastolic phase; segmenting a blood pool regionin the region of interest in first slice image at the end-diastolicphase; using the segmentation of the blood pool region in the region ofinterest in the first slice image at the end-diastolic phase as guidancefor segmenting a blood pool region in the region of interest in thesecond slice image at the end-diastolic phase; transforming the regionof interest in the first slice image at the end-diastolic phase into apolar coordinate image; performing a dual dynamic programming operationon the polar coordinate image to determine endocardial and epicardialboundaries of the myocardium in the polar coordinate image by selectingpoints with a minimum accumulated cost in the polar coordinate image tofind optimal paths representing the endocardial and epicardialboundaries of the myocardium in the polar coordinate image; andtransforming the polar coordinate image into a Cartesian coordinateimage to obtain the endocardial and epicardial boundaries of themyocardium in the first slice image at the end-diastolic phase.
 17. Themethod of claim 11, further comprising segmenting an endocardium and anepicardium of the myocardium in one of the slice images at the pluralityof time phases.
 18. The method of claim 17, wherein the segmenting anendocardium and an epicardium of the myocardium in one of the sliceimages at the plurality of time phases comprises: obtaining a currentslice image and a prior slice image from the slice images at theplurality of time phases, the current slice image and the prior sliceimage being the same one of the slice images at two adjacent time phasesrespectively, the current slice image corresponding to a current timephase, the prior slice image corresponding to a prior time phase, andthe current time phase being immediately subsequent to the prior timephase; transforming the current slice image into a current polarcoordinate image enhancing the current slice image; transforming theenhanced current slice image into an enhanced current polar coordinateimage; performing the dual dynamic programming operation on the currentpolar coordinate image and the enhanced current polar coordinate imageto determine optimal curves of the endocardial and epicardial boundariesof the myocardium of the left ventricle in the current polar coordinateimage and the enhanced current polar coordinate image, wherein thedetermination of the endocardial and epicardial boundaries of themyocardium of the left ventricle in the current polar coordinate imageand the enhanced current polar coordinate image is restricted byendocardial and epicardial boundaries of the myocardium of the leftventricle in the prior slice image at the prior time phase.
 19. Themethod of claim 18, wherein the enhancing the current slice imagecomprises: multiplying a gray value of a pixel in the current sliceimage by a value of a gray value distribution corresponding to the grayvalue of the pixel, wherein the gray value distribution is determined bythe slice images of the myocardium from the end-diastolic phase to theprior time phase in a same cardiac cycle.
 20. The method of claim 11,wherein the slice image at the first time phase other than themid-ventricular slice image at the first time phase is either locatedbetween the mid-ventricular slice image and an apical slice image orlocated between the mid-ventricular slice image and a basal slice image.